from IPython.display import Image, display
import os

# Visualiza 3 imágenes de entrenamiento
image_dir = "dataset_final_yolo_v8/images/train"
sample_images = os.listdir(image_dir)[:3]
for img in sample_images:
    display(Image(filename=os.path.join(image_dir, img)))

from pathlib import Path
import os

# Ruta base del dataset
dataset_path = Path("/home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data")
assert dataset_path.exists(), f"❌ Ruta no encontrada: {dataset_path}"

# Crear un archivo dataset.yaml reproducible
yaml_content = """
path: /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo
train: obj_train_data
val: obj_train_data
test: obj_train_data

names:
  0: iguana
  1: lobo_marino
  2: pelícano
  3: pingüino
  4: tortuga
"""

with open("dataset.yaml", "w") as f:
    f.write(yaml_content.strip())

print("✅ Archivo dataset.yaml generado correctamente.")

✅ Archivo dataset.yaml generado correctamente.

import os
os.remove("yolov8n.pt")

import torch
print(torch.__version__)  # ✅ Debe decir 2.1.2

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.2.6 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/runpy.py", line 198, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/runpy.py", line 88, in _run_code
    exec(code, run_globals)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel_launcher.py", line 18, in <module>
    app.launch_new_instance()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/traitlets/config/application.py", line 1075, in launch_instance
    app.start()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelapp.py", line 739, in start
    self.io_loop.start()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/tornado/platform/asyncio.py", line 211, in start
    self.asyncio_loop.run_forever()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/asyncio/base_events.py", line 608, in run_forever
    self._run_once()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/asyncio/base_events.py", line 1936, in _run_once
    handle._run()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/asyncio/events.py", line 84, in _run
    self._context.run(self._callback, *self._args)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 516, in dispatch_queue
    await self.process_one()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 505, in process_one
    await dispatch(*args)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 397, in dispatch_shell
    await result
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/ipkernel.py", line 368, in execute_request
    await super().execute_request(stream, ident, parent)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 752, in execute_request
    reply_content = await reply_content
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/ipkernel.py", line 455, in do_execute
    res = shell.run_cell(
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/zmqshell.py", line 577, in run_cell
    return super().run_cell(*args, **kwargs)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3116, in run_cell
    result = self._run_cell(
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3171, in _run_cell
    result = runner(coro)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/async_helpers.py", line 128, in _pseudo_sync_runner
    coro.send(None)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3394, in run_cell_async
    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3639, in run_ast_nodes
    if await self.run_code(code, result, async_=asy):
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3699, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "/tmp/ipykernel_110141/4193278548.py", line 1, in <module>
    import torch
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/__init__.py", line 1382, in <module>
    from .functional import *  # noqa: F403
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/functional.py", line 7, in <module>
    import torch.nn.functional as F
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/nn/__init__.py", line 1, in <module>
    from .modules import *  # noqa: F403
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/nn/modules/__init__.py", line 35, in <module>
    from .transformer import TransformerEncoder, TransformerDecoder, \
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/nn/modules/transformer.py", line 20, in <module>
    device: torch.device = torch.device(torch._C._get_default_device()),  # torch.device('cpu'),
/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/nn/modules/transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:84.)
  device: torch.device = torch.device(torch._C._get_default_device()),  # torch.device('cpu'),

2.1.2+cu121

from ultralytics import YOLO

# Carga directa sin errores
model = YOLO("yolov8n.pt")

!pip uninstall numpy -y
!pip install numpy==1.24.4

Found existing installation: numpy 2.2.6
Uninstalling numpy-2.2.6:
  Successfully uninstalled numpy-2.2.6
Collecting numpy==1.24.4
  Downloading numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB)
Downloading numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 17.3/17.3 MB 53.5 MB/s eta 0:00:00MB/s eta 0:00:01
Installing collected packages: numpy
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
scipy 1.16.1 requires numpy<2.6,>=1.25.2, but you have numpy 1.24.4 which is incompatible.
contourpy 1.3.3 requires numpy>=1.25, but you have numpy 1.24.4 which is incompatible.
opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= "3.9", but you have numpy 1.24.4 which is incompatible.
Successfully installed numpy-1.24.4

import numpy as np
import scipy
print("NumPy:", np.__version__)
print("SciPy:", scipy.__version__)

NumPy: 1.24.4
SciPy: 1.11.4

# Entrenar el modelo sobre el dataset personalizado
from ultralytics import YOLO

model = YOLO("yolov8n.pt")  # Modelo base (nano)

results = model.train(
    data="dataset.yaml",        # Tu archivo .yaml con clases y rutas
    epochs=100,                 # Número de épocas
    imgsz=640,                  # Tamaño de imagen (puedes aumentar a 800 o 960 si tienes GPU potente)
    batch=8,                    # Tamaño de lote
    name="galapagos_yolov8n",   # Nombre del experimento
    project="runs/train",       # Carpeta de resultados
    optimizer="Adam",           # Optimizador (puede cambiarse por SGD)
    device=0                    # GPU 0 (usa 'cpu' si no tienes GPU)
)

New https://pypi.org/project/ultralytics/8.3.170 available 😃 Update with 'pip install -U ultralytics'
Ultralytics YOLOv8.1.0 🚀 Python-3.11.13 torch-2.1.2+cu121 CUDA:0 (Quadro P1000, 4032MiB)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=dataset.yaml, epochs=100, time=None, patience=50, batch=8, imgsz=640, save=True, save_period=-1, cache=False, device=0, workers=8, project=runs/train, name=galapagos_yolov8n10, exist_ok=False, pretrained=True, optimizer=Adam, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/train/galapagos_yolov8n10
Overriding model.yaml nc=80 with nc=5

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  
 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 
 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 
 22        [15, 18, 21]  1    752287  ultralytics.nn.modules.head.Detect           [5, [64, 128, 256]]           
Model summary: 225 layers, 3011823 parameters, 3011807 gradients, 8.2 GFLOPs

/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/cuda/__init__.py:611: UserWarning: Can't initialize NVML
  warnings.warn("Can't initialize NVML")

Transferred 319/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅

train: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

val: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

Plotting labels to runs/train/galapagos_yolov8n10/labels.jpg... 
optimizer: Adam(lr=0.01, momentum=0.937) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
100 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size

      1/100      1.24G      1.736      3.918      1.886          2        640: 100%|██████████| 19/19 [00:07<00:00,  2.49it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 10/10 [00:02<00:00,  4.44it/s]

results = model.train(
    data="dataset.yaml",
    epochs=200,            # Más épocas
    imgsz=800,             # Imágenes más grandes
    batch=8,
    name="galapagos_yolov8n_aug",
    mosaic=1.0,            # Activar mosaic augmentation
    hsv_h=0.015, hsv_s=0.7, hsv_v=0.4,  # Perturbaciones de color
    flipud=0.5, fliplr=0.5,  # Volteos horizontales y verticales
    degrees=10, translate=0.1, scale=0.5, shear=2.0,
    device=0
)

New https://pypi.org/project/ultralytics/8.3.170 available 😃 Update with 'pip install -U ultralytics'
Ultralytics YOLOv8.1.0 🚀 Python-3.11.13 torch-2.1.2+cu121 CUDA:0 (Quadro P1000, 4032MiB)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=dataset.yaml, epochs=200, time=None, patience=50, batch=8, imgsz=800, save=True, save_period=-1, cache=False, device=0, workers=8, project=runs/train, name=galapagos_yolov8n_aug, exist_ok=False, pretrained=True, optimizer=Adam, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=10, translate=0.1, scale=0.5, shear=2.0, perspective=0.0, flipud=0.5, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/train/galapagos_yolov8n_aug

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  
 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 
 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 
 22        [15, 18, 21]  1    752287  ultralytics.nn.modules.head.Detect           [5, [64, 128, 256]]           
Model summary: 225 layers, 3011823 parameters, 3011807 gradients, 8.2 GFLOPs

Transferred 355/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅

train: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

val: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

Plotting labels to runs/train/galapagos_yolov8n_aug/labels.jpg... 
optimizer: Adam(lr=0.01, momentum=0.937) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
200 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size

      1/200      1.86G       2.23      3.242      2.503          4        800: 100%|██████████| 19/19 [00:11<00:00,  1.66it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 10/10 [00:03<00:00,  2.80it/s]

                   all        147        198      0.336      0.133      0.103     0.0442

!pip install huggingface_hub

Collecting huggingface_hub
  Downloading huggingface_hub-0.34.3-py3-none-any.whl.metadata (14 kB)
Requirement already satisfied: filelock in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from huggingface_hub) (3.18.0)
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Requirement already satisfied: packaging>=20.9 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from huggingface_hub) (25.0)
Requirement already satisfied: pyyaml>=5.1 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from huggingface_hub) (6.0.2)
Requirement already satisfied: requests in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from huggingface_hub) (2.32.4)
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Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from huggingface_hub) (4.14.1)
Collecting hf-xet<2.0.0,>=1.1.3 (from huggingface_hub)
  Downloading hf_xet-1.1.5-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (879 bytes)
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Downloading huggingface_hub-0.34.3-py3-none-any.whl (558 kB)
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Installing collected packages: hf-xet, huggingface_hub
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Successfully installed hf-xet-1.1.5 huggingface_hub-0.34.3

from ultralytics import YOLO

# Carga el modelo YOLOv10m preentrenado
model = YOLO("yolov10m.pt")

# Entrena con tu dataset personalizado
results = model.train(
    data="dataset.yaml",          # Ruta al archivo de configuración del dataset
    epochs=200,                   # Aumentar si se requiere mejor convergencia
    imgsz=640,                    # Resolución de entrada razonable
    batch=4,                      # Ajustado para no saturar GPU
    name="galapagos_yolov10m",    # Nombre del experimento
    project="runs/train",         # Carpeta de salida
    device=0,                     # GPU 0 (o 'cpu' si no tienes CUDA)
    optimizer="Adam",             # Optimización estable
    workers=2,                    # Número de hilos de carga
    patience=30                   # Early stopping si no mejora
)

/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

New https://pypi.org/project/ultralytics/8.3.170 available 😃 Update with 'pip install -U ultralytics'
Ultralytics YOLOv8.1.34 🚀 Python-3.11.13 torch-2.1.2+cu121 CUDA:0 (Quadro P1000, 4032MiB)
engine/trainer: task=detect, mode=train, model=yolov10m.pt, data=dataset.yaml, epochs=200, time=None, patience=30, batch=4, imgsz=640, save=True, save_period=-1, val_period=1, cache=False, device=0, workers=2, project=runs/train, name=galapagos_yolov10m3, exist_ok=False, pretrained=True, optimizer=Adam, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/train/galapagos_yolov10m3
Overriding model.yaml nc=80 with nc=5

                   from  n    params  module                                       arguments                     
  0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 
  1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                
  2                  -1  2    111360  ultralytics.nn.modules.block.C2f             [96, 96, 2, True]             
  3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               
  4                  -1  4    813312  ultralytics.nn.modules.block.C2f             [192, 192, 4, True]           
  5                  -1  1     78720  ultralytics.nn.modules.block.SCDown          [192, 384, 3, 2]              
  6                  -1  4   3248640  ultralytics.nn.modules.block.C2f             [384, 384, 4, True]           
  7                  -1  1    228672  ultralytics.nn.modules.block.SCDown          [384, 576, 3, 2]              
  8                  -1  2   1689984  ultralytics.nn.modules.block.C2fCIB          [576, 576, 2, True]           
  9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 
 10                  -1  1   1253088  ultralytics.nn.modules.block.PSA             [576, 576]                    
 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 13                  -1  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 
 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 16                  -1  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 
 17                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              
 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 19                  -1  2    831744  ultralytics.nn.modules.block.C2fCIB          [576, 384, 2, True]           
 20                  -1  1    152448  ultralytics.nn.modules.block.SCDown          [384, 384, 3, 2]              
 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 22                  -1  2   1911168  ultralytics.nn.modules.block.C2fCIB          [960, 576, 2, True]           
 23        [16, 19, 22]  1   2286766  ultralytics.nn.modules.head.v10Detect        [5, [192, 384, 576]]          
YOLOv10m summary: 498 layers, 16489918 parameters, 16489902 gradients, 64.0 GFLOPs

Transferred 787/799 items from pretrained weights
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅

train: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

val: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

Plotting labels to runs/train/galapagos_yolov10m3/labels.jpg... 
optimizer: Adam(lr=0.01, momentum=0.937) with parameter groups 129 weight(decay=0.0), 142 weight(decay=0.0005), 141 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to runs/train/galapagos_yolov10m3
Starting training for 200 epochs...

      Epoch    GPU_mem     box_om     cls_om     dfl_om     box_oo     cls_oo     dfl_oo  Instances       Size

      1/200      2.83G      2.106      9.078      2.391      1.917      72.19      2.263         19        640: 100%|██████████| 37/37 [00:51<00:00,  1.38s/it]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 19/19 [00:08<00:00,  2.15it/s]

                   all        147        198          0          0          0          0

from ultralytics import YOLO

# Cargar el modelo YOLOv10m preentrenado
model = YOLO("yolov10m.pt")

# Entrenamiento con aumento de datos y early stopping
results = model.train(
    data="dataset.yaml",           # Configuración del dataset
    epochs=300,                    # Aumentar a 300 épocas
    imgsz=640,                     # Resolución de entrada
    batch=4,                       # Ajustado para tu GPU (3.9GB)
    name="galapagos_yolov10m_300e_aug",  # Nombre del experimento
    project="runs/train",         # Carpeta de salida
    device=0,                     # GPU 0
    optimizer="Adam",             # Optimizador
    workers=2,                    # Hilos de carga
    augment=True,                 # Aumento de datos activado
    patience=50,                  # Early stopping si no mejora
    verbose=True                  # Mostrar progreso detallado
)

/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

New https://pypi.org/project/ultralytics/8.3.170 available 😃 Update with 'pip install -U ultralytics'
Ultralytics YOLOv8.1.34 🚀 Python-3.11.13 torch-2.1.2+cu121 CUDA:0 (Quadro P1000, 4032MiB)
engine/trainer: task=detect, mode=train, model=yolov10m.pt, data=dataset.yaml, epochs=300, time=None, patience=50, batch=4, imgsz=640, save=True, save_period=-1, val_period=1, cache=False, device=0, workers=2, project=runs/train, name=galapagos_yolov10m_300e_aug2, exist_ok=False, pretrained=True, optimizer=Adam, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=True, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/train/galapagos_yolov10m_300e_aug2
Overriding model.yaml nc=80 with nc=5

                   from  n    params  module                                       arguments                     
  0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 
  1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                
  2                  -1  2    111360  ultralytics.nn.modules.block.C2f             [96, 96, 2, True]             
  3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               
  4                  -1  4    813312  ultralytics.nn.modules.block.C2f             [192, 192, 4, True]           
  5                  -1  1     78720  ultralytics.nn.modules.block.SCDown          [192, 384, 3, 2]              
  6                  -1  4   3248640  ultralytics.nn.modules.block.C2f             [384, 384, 4, True]           
  7                  -1  1    228672  ultralytics.nn.modules.block.SCDown          [384, 576, 3, 2]              
  8                  -1  2   1689984  ultralytics.nn.modules.block.C2fCIB          [576, 576, 2, True]           
  9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 
 10                  -1  1   1253088  ultralytics.nn.modules.block.PSA             [576, 576]                    
 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 13                  -1  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 
 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 16                  -1  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 
 17                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              
 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 19                  -1  2    831744  ultralytics.nn.modules.block.C2fCIB          [576, 384, 2, True]           
 20                  -1  1    152448  ultralytics.nn.modules.block.SCDown          [384, 384, 3, 2]              
 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 22                  -1  2   1911168  ultralytics.nn.modules.block.C2fCIB          [960, 576, 2, True]           
 23        [16, 19, 22]  1   2286766  ultralytics.nn.modules.head.v10Detect        [5, [192, 384, 576]]          
YOLOv10m summary: 498 layers, 16489918 parameters, 16489902 gradients, 64.0 GFLOPs

Transferred 787/799 items from pretrained weights
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅

train: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

val: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'

Plotting labels to runs/train/galapagos_yolov10m_300e_aug2/labels.jpg... 
optimizer: Adam(lr=0.01, momentum=0.937) with parameter groups 129 weight(decay=0.0), 142 weight(decay=0.0005), 141 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to runs/train/galapagos_yolov10m_300e_aug2
Starting training for 300 epochs...

      Epoch    GPU_mem     box_om     cls_om     dfl_om     box_oo     cls_oo     dfl_oo  Instances       Size

      1/300      2.83G      2.106      9.078      2.391      1.917      72.19      2.263         19        640: 100%|██████████| 37/37 [00:54<00:00,  1.47s/it]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 19/19 [00:09<00:00,  1.93it/s]

                   all        147        198          0          0          0          0

!pip install --upgrade ultralytics

Requirement already satisfied: ultralytics in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (8.1.34)
Collecting ultralytics
  Downloading ultralytics-8.3.171-py3-none-any.whl.metadata (37 kB)
Requirement already satisfied: numpy>=1.23.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (1.26.4)
Requirement already satisfied: matplotlib>=3.3.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (3.10.3)
Requirement already satisfied: opencv-python>=4.6.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (4.12.0.88)
Requirement already satisfied: pillow>=7.1.2 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (11.3.0)
Requirement already satisfied: pyyaml>=5.3.1 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (6.0.2)
Requirement already satisfied: requests>=2.23.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (2.32.4)
Requirement already satisfied: scipy>=1.4.1 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (1.11.4)
Requirement already satisfied: torch>=1.8.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (2.1.2)
Requirement already satisfied: torchvision>=0.9.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (0.16.2)
Requirement already satisfied: tqdm>=4.64.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (4.67.1)
Requirement already satisfied: psutil in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (7.0.0)
Requirement already satisfied: py-cpuinfo in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (9.0.0)
Requirement already satisfied: pandas>=1.1.4 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (2.3.1)
Collecting ultralytics-thop>=2.0.0 (from ultralytics)
  Using cached ultralytics_thop-2.0.14-py3-none-any.whl.metadata (9.4 kB)
Requirement already satisfied: contourpy>=1.0.1 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from matplotlib>=3.3.0->ultralytics) (1.3.3)
Requirement already satisfied: cycler>=0.10 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from matplotlib>=3.3.0->ultralytics) (4.59.0)
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Requirement already satisfied: python-dateutil>=2.7 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from matplotlib>=3.3.0->ultralytics) (2.9.0.post0)
Collecting numpy>=1.23.0 (from ultralytics)
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INFO: pip is looking at multiple versions of scipy to determine which version is compatible with other requirements. This could take a while.
Collecting scipy>=1.4.1 (from ultralytics)
  Using cached scipy-1.16.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (61 kB)
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Requirement already satisfied: typing-extensions in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from torch>=1.8.0->ultralytics) (4.14.1)
Requirement already satisfied: sympy in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from torch>=1.8.0->ultralytics) (1.14.0)
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Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from torch>=1.8.0->ultralytics) (12.1.105)
Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from torch>=1.8.0->ultralytics) (12.1.105)
Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from torch>=1.8.0->ultralytics) (8.9.2.26)
Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from torch>=1.8.0->ultralytics) (12.1.3.1)
Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from torch>=1.8.0->ultralytics) (11.0.2.54)
Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from torch>=1.8.0->ultralytics) (10.3.2.106)
Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from torch>=1.8.0->ultralytics) (11.4.5.107)
Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from torch>=1.8.0->ultralytics) (12.1.0.106)
Requirement already satisfied: nvidia-nccl-cu12==2.18.1 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from torch>=1.8.0->ultralytics) (2.18.1)
Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from torch>=1.8.0->ultralytics) (12.1.105)
Requirement already satisfied: triton==2.1.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from torch>=1.8.0->ultralytics) (2.1.0)
Requirement already satisfied: nvidia-nvjitlink-cu12 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.8.0->ultralytics) (12.6.85)
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Requirement already satisfied: mpmath<1.4,>=1.1.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)
Downloading ultralytics-8.3.171-py3-none-any.whl (1.0 MB)
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Using cached scipy-1.16.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (35.4 MB)
Using cached ultralytics_thop-2.0.14-py3-none-any.whl (26 kB)
Installing collected packages: numpy, scipy, ultralytics-thop, ultralytics
  Attempting uninstall: numpy
    Found existing installation: numpy 1.26.4
    Uninstalling numpy-1.26.4:
      Successfully uninstalled numpy-1.26.4
  Attempting uninstall: scipy━━━━━━━━━━━━━━━━━━━━━━━━━ 0/4 [numpy]
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  Attempting uninstall: ultralytics╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/4 [scipy]
    Found existing installation: ultralytics 8.1.34;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/4 [scipy]
    Uninstalling ultralytics-8.1.34:5;237m╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/4 [scipy]
      Successfully uninstalled ultralytics-8.1.3438;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/4 [scipy]
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Successfully installed numpy-2.2.6 scipy-1.16.1 ultralytics-8.3.171 ultralytics-thop-2.0.14

import ultralytics
print(ultralytics.__version__)

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.2.6 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/runpy.py", line 198, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/runpy.py", line 88, in _run_code
    exec(code, run_globals)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel_launcher.py", line 18, in <module>
    app.launch_new_instance()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/traitlets/config/application.py", line 1075, in launch_instance
    app.start()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelapp.py", line 739, in start
    self.io_loop.start()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/tornado/platform/asyncio.py", line 211, in start
    self.asyncio_loop.run_forever()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/asyncio/base_events.py", line 608, in run_forever
    self._run_once()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/asyncio/base_events.py", line 1936, in _run_once
    handle._run()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/asyncio/events.py", line 84, in _run
    self._context.run(self._callback, *self._args)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 516, in dispatch_queue
    await self.process_one()
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 505, in process_one
    await dispatch(*args)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 397, in dispatch_shell
    await result
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/ipkernel.py", line 368, in execute_request
    await super().execute_request(stream, ident, parent)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/kernelbase.py", line 752, in execute_request
    reply_content = await reply_content
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/ipkernel.py", line 455, in do_execute
    res = shell.run_cell(
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ipykernel/zmqshell.py", line 577, in run_cell
    return super().run_cell(*args, **kwargs)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3116, in run_cell
    result = self._run_cell(
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3171, in _run_cell
    result = runner(coro)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/async_helpers.py", line 128, in _pseudo_sync_runner
    coro.send(None)
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3394, in run_cell_async
    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3639, in run_ast_nodes
    if await self.run_code(code, result, async_=asy):
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3699, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "/tmp/ipykernel_75171/860101959.py", line 1, in <module>
    import ultralytics
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ultralytics/__init__.py", line 11, in <module>
    from ultralytics.models import NAS, RTDETR, SAM, YOLO, YOLOE, FastSAM, YOLOWorld
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ultralytics/models/__init__.py", line 3, in <module>
    from .fastsam import FastSAM
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ultralytics/models/fastsam/__init__.py", line 3, in <module>
    from .model import FastSAM
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ultralytics/models/fastsam/model.py", line 6, in <module>
    from ultralytics.engine.model import Model
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/ultralytics/engine/model.py", line 8, in <module>
    import torch
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/__init__.py", line 1382, in <module>
    from .functional import *  # noqa: F403
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/functional.py", line 7, in <module>
    import torch.nn.functional as F
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/nn/__init__.py", line 1, in <module>
    from .modules import *  # noqa: F403
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/nn/modules/__init__.py", line 35, in <module>
    from .transformer import TransformerEncoder, TransformerDecoder, \
  File "/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/nn/modules/transformer.py", line 20, in <module>
    device: torch.device = torch.device(torch._C._get_default_device()),  # torch.device('cpu'),
/home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages/torch/nn/modules/transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:84.)
  device: torch.device = torch.device(torch._C._get_default_device()),  # torch.device('cpu'),

8.3.171

!pip install --upgrade ultralytics

Requirement already satisfied: ultralytics in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (8.3.171)
Requirement already satisfied: numpy>=1.23.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (2.2.6)
Requirement already satisfied: matplotlib>=3.3.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (3.10.3)
Requirement already satisfied: opencv-python>=4.6.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (4.12.0.88)
Requirement already satisfied: pillow>=7.1.2 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (11.3.0)
Requirement already satisfied: pyyaml>=5.3.1 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (6.0.2)
Requirement already satisfied: requests>=2.23.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (2.32.4)
Requirement already satisfied: scipy>=1.4.1 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (1.16.1)
Requirement already satisfied: torch>=1.8.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (2.1.2)
Requirement already satisfied: torchvision>=0.9.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (0.16.2)
Requirement already satisfied: tqdm>=4.64.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (4.67.1)
Requirement already satisfied: psutil in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (7.0.0)
Requirement already satisfied: py-cpuinfo in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (9.0.0)
Requirement already satisfied: pandas>=1.1.4 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (2.3.1)
Requirement already satisfied: ultralytics-thop>=2.0.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from ultralytics) (2.0.14)
Requirement already satisfied: contourpy>=1.0.1 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from matplotlib>=3.3.0->ultralytics) (1.3.3)
Requirement already satisfied: cycler>=0.10 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from matplotlib>=3.3.0->ultralytics) (4.59.0)
Requirement already satisfied: kiwisolver>=1.3.1 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from matplotlib>=3.3.0->ultralytics) (1.4.8)
Requirement already satisfied: packaging>=20.0 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from matplotlib>=3.3.0->ultralytics) (25.0)
Requirement already satisfied: pyparsing>=2.3.1 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from matplotlib>=3.3.0->ultralytics) (3.2.3)
Requirement already satisfied: python-dateutil>=2.7 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from matplotlib>=3.3.0->ultralytics) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in /home/jdominguez/anaconda3/envs/yolov8-galapagos/lib/python3.11/site-packages (from pandas>=1.1.4->ultralytics) (2025.2)
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!pip uninstall -y numpy
!pip install numpy==2.2.0 opencv-python==4.12.0.88 --force-reinstall --no-cache-dir

Found existing installation: numpy 2.2.0
Uninstalling numpy-2.2.0:
  Successfully uninstalled numpy-2.2.0
Collecting numpy==2.2.0
  Downloading numpy-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (62 kB)
Collecting opencv-python==4.12.0.88
  Downloading opencv_python-4.12.0.88-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (19 kB)
Downloading numpy-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.4 MB)
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Downloading opencv_python-4.12.0.88-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (67.0 MB)
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Installing collected packages: numpy, opencv-python
  Attempting uninstall: opencv-python━━━━━━━━━━━━━━━━━ 0/2 [numpy]
    Found existing installation: opencv-python 4.12.0.88[32m0/2 [numpy]
    Uninstalling opencv-python-4.12.0.88:━━━━━━━━━ 0/2 [numpy]
      Successfully uninstalled opencv-python-4.12.0.88 0/2 [numpy]
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Successfully installed numpy-2.2.0 opencv-python-4.12.0.88

import numpy as np
import torch
print("NumPy:", np.__version__)
print("Torch:", torch.__version__)

NumPy: 2.2.6
Torch: 2.7.1+cu118

import torch
torch.cuda.empty_cache()

from ultralytics import YOLO

model = YOLO("yolo11l.pt")

model.train(
    data="dataset.yaml",
    epochs=300,
    imgsz=640,
    batch=1,                 # CPU tiene menos RAM, mejor con batch pequeño
    device="cpu",            # ✅ fuerza el uso de CPU
    name="galapagos_yolo11l_cpu",
    optimizer="auto",
    lr0=0.01,
    momentum=0.937,
    weight_decay=0.001,
    patience=50,
    seed=42,
    amp=False
)

Ultralytics 8.3.171 🚀 Python-3.11.13 torch-2.7.1+cu118 CPU (Intel Xeon E-2246G 3.60GHz)
engine/trainer: agnostic_nms=False, amp=False, augment=False, auto_augment=randaugment, batch=1, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=dataset.yaml, degrees=0.0, deterministic=True, device=cpu, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=300, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolo11l.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=galapagos_yolo11l_cpu, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=50, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/detect/galapagos_yolo11l_cpu, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=42, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.001, workers=8, workspace=None
Overriding model.yaml nc=80 with nc=5

                   from  n    params  module                                       arguments                     
  0                  -1  1      1856  ultralytics.nn.modules.conv.Conv             [3, 64, 3, 2]                 
  1                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  2                  -1  2    173824  ultralytics.nn.modules.block.C3k2            [128, 256, 2, True, 0.25]     
  3                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              
  4                  -1  2    691712  ultralytics.nn.modules.block.C3k2            [256, 512, 2, True, 0.25]     
  5                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              
  6                  -1  2   2234368  ultralytics.nn.modules.block.C3k2            [512, 512, 2, True]           
  7                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              
  8                  -1  2   2234368  ultralytics.nn.modules.block.C3k2            [512, 512, 2, True]           
  9                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 5]                 
 10                  -1  2   1455616  ultralytics.nn.modules.block.C2PSA           [512, 512, 2]                 
 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 13                  -1  2   2496512  ultralytics.nn.modules.block.C3k2            [1024, 512, 2, True]          
 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 16                  -1  2    756736  ultralytics.nn.modules.block.C3k2            [1024, 256, 2, True]          
 17                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              
 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 19                  -1  2   2365440  ultralytics.nn.modules.block.C3k2            [768, 512, 2, True]           
 20                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              
 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 22                  -1  2   2496512  ultralytics.nn.modules.block.C3k2            [1024, 512, 2, True]          
 23        [16, 19, 22]  1   1414879  ultralytics.nn.modules.head.Detect           [5, [256, 512, 512]]          
YOLO11l summary: 357 layers, 25,314,335 parameters, 25,314,319 gradients, 87.3 GFLOPs

Transferred 1009/1015 items from pretrained weights
Freezing layer 'model.23.dfl.conv.weight'
train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 7609.2±1463.1 MB/s, size: 2661.5 KB)

train: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 3169.7±1413.0 MB/s, size: 2935.6 KB)

val: Scanning /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data.cache... 148 images, 14 backgrounds, 1 corrupt: 100%|██████████| 148/148 [00:00<?, ?it/s]

train: WARNING ⚠️ /home/jdominguez/Documentos/Cuadernos/PaulVisionDCC/dataset_final_yolo/dataset_final_yolo/obj_train_data/Zalophus_wollebaeki_9.jpg: ignoring corrupt image/label: could not convert string to float: '0.551530g'
Plotting labels to runs/detect/galapagos_yolo11l_cpu/labels.jpg...

optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... 
optimizer: AdamW(lr=0.001111, momentum=0.9) with parameter groups 167 weight(decay=0.0), 174 weight(decay=0.001), 173 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 0 dataloader workers
Logging results to runs/detect/galapagos_yolo11l_cpu
Starting training for 300 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size

      1/300         0G      1.425      4.491      1.752          1        640: 100%|██████████| 147/147 [03:20<00:00,  1.36s/it]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 74/74 [00:51<00:00,  1.44it/s]

                   all        147        198     0.0304      0.096     0.0172    0.00818

Entrenamiento de YOLOv8 con Transfer Learning - Especies Galápagos 🦎🐧¶